Ramp rate handling strategies in Dynamic Economic Load Dispatch (DELD) problem using Grey Wolf Optimizer (GWO)

Sattar, Muhammad Kashif; Ahmad, Aftab; Fayyaz, Saqib; Haq, Syed Sadam Ul; Saddique, Muhammad Shahzar

doi:10.1080/02533839.2019.1694446

Cited by 17 publications

(12 citation statements)

References 36 publications

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“…The conventional economic load dispatch (ELD) refers to minimizing the thermal generation costs for a fixed load demand in a certain time slot (e.g. 1 h) [3]. In order to plan more accurate and comprehensive power dispatch in the power system, dynamic economic load dispatch (DELD) is introduced [3].…”

Section: Dynamic Economic Load Dispatch (Deld) Problem Formulationmentioning

confidence: 99%

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A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints

Alanazi

2021

IEEE Access

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Dynamic economic load dispatch (DELD), which determines the best generation scheduling for different power plants during the next 24 hours in order to meet the electricity demand as well as minimize the total energy cost, is a highly complex non-linear and non-convex problem. This study proposes an efficient modified teaching-learning-based optimization algorithm, called MTLBO, to effectively solve the DELD optimization problem in a power system containing thermal generations and wind power. The planning problem comprises the total fuel cost function with valve-point loading effect and the transmission power losses. Also, the uncertainties of wind power and load demand along with the various equality and inequality operational constraints such as power generation limits, ramp rate limits, prohibited operating zones and power balance are considered in the problem. Additionally, in the proposed MTLBO algorithm, the learning phase is properly integrated into the teaching phase to improve the convergence characteristic of the original TLBO. Moreover, in order to enhance the feature of local optima avoidance, interaction of up to five students are incorporated into the learning phase to improve the knowledge of each student. To exhibit the effectiveness of the proposed approach, the algorithm is applied into the 14 real test functions. In addition, several cases with 10 and 30 unit test systems are investigated over the planning period. Compared to the main TLBO algorithm which is mostly used optimization algorithms proposed in prior studies, the simulation results demonstrate the efficiency and superiority of the proposed optimization approach in terms of consistency, robustness, convergence rate and finding better plausible optimal solutions. INDEX TERMS Dynamic economic load dispatch, modified teaching learning based optimization, load demand, operational constraints, optimal solutions, real-world optimization problems, total fuel cost function, transmission power loss, uncertainty, valve-point loading effect, wind power.

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Section: Dynamic Economic Load Dispatch (Deld) Problem Formulationmentioning

confidence: 99%

“…1 h) [3]. In order to plan more accurate and comprehensive power dispatch in the power system, dynamic economic load dispatch (DELD) is introduced [3]. As mentioned previously, DELD is a real-time optimization problem which plans the optimal power supply dispatch for a 24-hour period.…”

Section: Dynamic Economic Load Dispatch (Deld) Problem Formulationmentioning

confidence: 99%

A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints

Alanazi

2021

IEEE Access

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“…All these algorithms are population-based strategies having fixed population and they locate the optimum solution within a search space using two distinct stages of search namely local and global search [46]. All these algorithms were successful in achieving solution of the desired problem with varying degree of accuracy and time [47,48]. Despite the achievement of a successful solution from these techniques the research for a better solution is continuous because of availability of new solution techniques being developed and the opportunity in optimized solution outlined by no free lunch theorem (NFL) [49,50].…”

Section: Introductionmentioning

confidence: 99%

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

et al. 2021

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This paper proposes a novel improved polar bear optimization (IPBO) algorithm and employs it along with polar bear optimization (PBO) and chaotic population-based variants of polar bear optimization algorithm to solve combined economic emission dispatch (CEED) problem. PBO is a meta-heuristic technique inspired by the hunting mechanisms of polar bears in harsh arctic regions based only on their sense of sight. Polar bears in nature exhibits hunting of prey not only on their sight but also on their keen sense of smell. Hence, a novel improved variant of PBO which enhances its operation by equipping it with tracking capabilities utilizing polar bears sense of smell has been proposed in this study. The validity of novel IPBO is tested through 5 benchmark functions and 140 units Korean ED problem. Furthermore, the impact of different population initialization methods is also observed on the capabilities of conventional PBO. The proposed chaotic population based PBO, improved PBO (IPBO) and PBO are employed to solve IEEE 3 unit and 6-unit CEED problem. CEED is a multi-objective power system optimization problem with conflicting objectives of cost and emission. The simulations performed undertake each objective individually as well as collectively. The results achieved by each technique are analyzed statistically through Wilcoxon rank sum test (WRST), probability density function and cumulative density function. Both the statistical and numerical analysis of results showcase the strength of each solution technique as well as their ability to improve cost and emissions in the solution of CEED problem.

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“…The different constraints associated with the UC problem are load demand, reserve balance constraints, power generation limits, minimum up and down time constraints, and the ramp‐rate constraints, which lead to a more complex optimization. Different variants of ramp rate handling strategies were discussed in Reference 2. In an electric power system, the load demand varies during day time, resulting in different peak values.…”

Section: Introductionmentioning

confidence: 99%

A novel binary variant model of swarm inspired polar bear optimization algorithm employed for scalable unit commitment

Haq

Ahmad

Ikram

et al. 2020

Int Trans Electr Energ Syst

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Summary For an effective and reliable power system, determining the optimal state of the accessible producing units is the key step, which comprises of two segments; making decision in committing the generators (unit commitment) followed by the decision‐making of their active power allocation (economic dispatch). Unit commitment (UC) problem is a mixed‐integer bit transition‐state optimization problem to which classical optimizing tools strive to evaluate, whereas metaheuristic methods prove to be a healthy attempt. In this study, a binary model of the polar bear optimization (BPBO) algorithm is developed and is utilized to solve this combinatorial problem. Polar bear optimization (PBO) is a newly developed swarm‐based approach, simulating three mechanisms in one technique in the form of effectual exploration and exploitation phase along with dynamic control of population size. BPBO uses a modified sigmoid conversion function to produce the state bits (ON/OFF) of the units. These three potent procedures help in determining the optimal transition state of units by varying the size of the search agents. For solving the economic dispatch problem (EDP), the conventional lambda iteration method is used. Repairing of the infeasible bit is also carried out by applying heuristic adjustments. To illustrate the vitality of the BPBO, it is modeled in “MATLAB 2016a” and tested on IEEE 4, 5, 10, 20, 40, 60, 80, and 100‐unit test systems. Comparative evaluation in terms of optimum combinations, active power allocation, and simulation time with other available methodologies is also done, resulting in a progressive approach with superiority to other recent methods.

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Ramp rate handling strategies in Dynamic Economic Load Dispatch (DELD) problem using Grey Wolf Optimizer (GWO)

Cited by 17 publications

References 36 publications

A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints

A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

A novel binary variant model of swarm inspired polar bear optimization algorithm employed for scalable unit commitment

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